Authors :
Nitin Liladhar Rane; Anand Achari; Saurabh P. Choudhary; Monica Giduturi
Volume/Issue :
Volume 8 - 2023, Issue 4 - April
Google Scholar :
https://bit.ly/43uxUln
Scribd :
https://bit.ly/43HYTKv
DOI :
https://doi.org/10.5281/zenodo.7845446
Abstract :
In this research paper, the effectiveness and
capability of remote sensing (RS) and geographic
information systems (GIS) are investigated as powerful
tools for analyzing changes in land use and land cover
(LULC), as well as for accuracy assessment. The study
employs the literature of satellite imagery and GIS data
to evaluate LULC changes over a period and to assess
the accuracy of the analysis. Moreover, the research
investigates the land use and land cover change detection
analysis using RS and GIS, application of
artificial intelligence (AI), and Machine Learning (ML)
in LULC classification, environment and risk evaluation,
stages of process LULC classification, factors affecting
the LULC classification, accuracy assessment, and
potential applications of RS and GIS in predicting future
LULC changes and supporting decision-making
processes. The findings of the study suggest that RS and
GIS are highly effective and accurate for LULC analysis
and assessment, with substantial potential for predicting
and managing future changes in land use and land cover.
The paper emphasizes the importance of utilizing RS
and GIS techniques in the field of sustainable
environmental management and resource planning.
Keywords :
Remote Sensing (RS); Geographic Information Systems (GIS); Land use and Land Cover (LULC); Accuracy Assessment; Scale; Resolution Effects.
In this research paper, the effectiveness and
capability of remote sensing (RS) and geographic
information systems (GIS) are investigated as powerful
tools for analyzing changes in land use and land cover
(LULC), as well as for accuracy assessment. The study
employs the literature of satellite imagery and GIS data
to evaluate LULC changes over a period and to assess
the accuracy of the analysis. Moreover, the research
investigates the land use and land cover change detection
analysis using RS and GIS, application of
artificial intelligence (AI), and Machine Learning (ML)
in LULC classification, environment and risk evaluation,
stages of process LULC classification, factors affecting
the LULC classification, accuracy assessment, and
potential applications of RS and GIS in predicting future
LULC changes and supporting decision-making
processes. The findings of the study suggest that RS and
GIS are highly effective and accurate for LULC analysis
and assessment, with substantial potential for predicting
and managing future changes in land use and land cover.
The paper emphasizes the importance of utilizing RS
and GIS techniques in the field of sustainable
environmental management and resource planning.
Keywords :
Remote Sensing (RS); Geographic Information Systems (GIS); Land use and Land Cover (LULC); Accuracy Assessment; Scale; Resolution Effects.
